Pseudo-Boolean Optimization: Theory and Applications in Vision
Year: 2012
Type: article
Abstract: Many problems in computer vision, such as stereo, segmentation and denoising can be formulated as pseudo-boolean optimization problems. Over the last decade, graphs cuts have become a standard tool for solving such problems. The last couple of years have seen a great advancement in the methods used to minimize pseudo-boolean functions of higher order than quadratic. In this paper, we give an overview of how one can optimize higher-order functions via generalized roof duality and how it can be applied to problems in image analysis and vision.
Authors: Petter Strandmark, Fredrik Kahl
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W1855173132